FT-IR and 2D-IR Spectroscopic Studies on the Effect of Ions on the Phase Separation Behavior of PVME Aqueous Solution

Effect of Nano-Silica and Sorbitol on the Properties of Chitosan-Based Composite Films

Chitosan and its derivatives are widely used in food packaging, pharmaceutical, biotechnology, medical, textile, paper, agriculture, and environmental industries. However, the flexibility of chitosan films is extremely poor, which limits its relevant applications to a large extent. In this paper, chitosan/sorbitol/nano-silica (CS/sorbitol/SiO2) composite films were prepared by the casting film method using chitosan, sorbitol, Tween-80 and nano-SiO2 as raw materials. The structure of the films was characterized by infrared spectroscopy, electron scanning microscopy, and X-ray diffraction analysis. The effects of sorbitol and nano-silica dosage on the mechanical properties, thermal properties and water vapor barrier properties of the composite film were investigated. The results show that with the gradual increase in sorbitol (≤75 wt %), the elongation at the break of chitosan/sorbitol films significantly increased. When the addition of sorbitol was 75 wt %, the elongation at break of the chitosan/sorbitol composite film was 13 times higher than that of the chitosan film. Moreover, nano-SiO2 can further improve the mechanical properties and thermal stability of the chitosan/sorbitol composite films. When the amount of nano-silica was 4.5 wt %, the composite film became more flexible, with a maximum elongation of 90.8% (which is 14 times that of chitosan film), and its toughness increased to 10.52 MJm-3 (which is 6 times that of chitosan film). This study balances the tensile strength and elongation at break of the composite films by adding a plasticizer and nano-filler, providing a reference for the preparation of chitosan composites or their blending with other polymers, and has practical guiding significance for the industrial production of biomass plastics.

Chemometrics and Related Fields in Python

The Python programing language is becoming a promising tool for data analysis in various fields. However, little attention has been paid to using Python in the field of analytical chemistry, though recent advances in instrumental analysis require robust and reliable data analysis. In order to overcome the difficulty in accurate analysis, multivariate analysis, or chemometrics, has been widely applied to various kinds of data obtained by instrumental analysis. In the present work, the potential usefulness of Python for chemometrics and related fields in chemistry is reviewed. Many practical tools for chemometrics, e.g., principal component analysis (PCA), partial least squares (PLS), support vector machine (SVM), etc., are included in the scikit-learn machine learning (ML) library for Python. Other useful libraries such as pyMCR for multivariate curve resolution (MCR), 2Dpy for two-dimensional correlation spectroscopy (2D-COS), etc. can be obtained from GitHub. For these reasons, a computational environment for chemometrics is easily constructed in Python.